# Node embedding frameworks

> AI-ranked search results for `node embedding frameworks` on awesome-repositories.com — ordered by an LLM for relevance, best match first. 117 total matches; showing the top 16.

Explore on the web: https://awesome-repositories.com/q/node-embedding-frameworks

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## Results

- [rusty1s/pytorch_geometric](https://awesome-repositories.com/repository/rusty1s-pytorch-geometric.md) (23,848 ⭐) — PyTorch Geometric is a library for building and training machine learning models on graph-structured data. It provides a framework for developing graph neural networks, including a specialized system for implementing node-to-node information exchange via customizable message passing, aggregation, and update functions.

The library includes a sparse data processing toolkit that utilizes accelerated CPU and CUDA kernels to perform efficient reductions on large sparse datasets. It supports the creation of specialized architectures for structured data such as 3D meshes and point clouds.

The proje
- [williamleif/graphsage](https://awesome-repositories.com/repository/williamleif-graphsage.md) (3,657 ⭐) — GraphSAGE is a graph neural network framework designed for inductive representation learning on large-scale graphs. It functions as an inductive graph embedding tool and neighborhood aggregation engine, enabling the generation of numerical node representations that generalize to previously unseen data.

The system distinguishes itself by computing node embeddings through the aggregation of features from local neighborhoods rather than relying on a global lookup table. This approach allows the framework to operate as both a supervised graph classifier for predicting categorical node classes and
- [dmlc/dgl](https://awesome-repositories.com/repository/dmlc-dgl.md) (14,283 ⭐) — DGL is a Python library for building and training graph neural networks. It functions as a graph message passing framework and a geometric deep learning tool, enabling the development of models that analyze graph-structured data.

The library is designed for large-scale graph processing, utilizing distributed training and neighbor sampling to handle datasets with billions of edges. It provides specialized support for heterogeneous graph modeling, allowing for the representation of complex real-world entities with multiple node and edge types.

Its capabilities cover a wide range of graph tasks
- [pyg-team/pytorch_geometric](https://awesome-repositories.com/repository/pyg-team-pytorch-geometric.md) (23,838 ⭐) — This project is a deep learning library designed for training neural networks on irregular data structures, including graphs, 3D meshes, and point clouds. It functions as an extension to the PyTorch framework, providing specialized layers and kernels that enable the processing of complex, non-Euclidean information.

The library distinguishes itself through a geometric deep learning toolkit that manages the unique requirements of graph-based data. It utilizes sparse matrix-based message passing to aggregate information across nodes and employs dynamic computational graph construction to accommo
- [tkipf/pygcn](https://awesome-repositories.com/repository/tkipf-pygcn.md) (5,404 ⭐) — pygcn is a PyTorch library and framework for implementing graph convolutional networks. It provides tools for semi-supervised node classification and the generation of node embeddings from graph-structured data.

The system converts graph nodes into low-dimensional vectors based on neighborhood patterns and local similarities. It enables the prediction of node labels by leveraging both a small set of labeled examples and the overall graph topology.

The library covers relational data analysis and semi-supervised graph learning. It includes computational primitives for message passing, adjacenc
- [stellargraph/stellargraph](https://awesome-repositories.com/repository/stellargraph-stellargraph.md) (3,061 ⭐) — StellarGraph - Machine Learning on Graphs
- [google-deepmind/graph_nets](https://awesome-repositories.com/repository/google-deepmind-graph-nets.md) (5,402 ⭐) — graph_nets is a graph-structured deep learning framework and library for building message-passing neural networks. It provides tools for designing architectures that operate on nodes and edges to process and reason about data structured as graphs using TensorFlow.

The framework implements a message-passing paradigm for iterative information exchange between nodes. This approach enables the development of models that can reason about complex graph-structured inputs for tasks such as path-finding and sorting, or serve as a predictor for the future states and trajectories of physical systems.
- [tkipf/gae](https://awesome-repositories.com/repository/tkipf-gae.md) (1,740 ⭐) — Implementation of Graph Auto-Encoders in TensorFlow
- [phanein/deepwalk](https://awesome-repositories.com/repository/phanein-deepwalk.md) (2,757 ⭐) — DeepWalk - Deep Learning for Graphs
- [snap-stanford/graphgym](https://awesome-repositories.com/repository/snap-stanford-graphgym.md) (1,893 ⭐) — Platform for designing and evaluating Graph Neural Networks (GNN)
- [tkipf/gcn](https://awesome-repositories.com/repository/tkipf-gcn.md) (7,361 ⭐) — This is a graph convolutional network library designed for performing node and graph classification on graph-structured data. It functions as a framework for generating graph embeddings and implementing spectral convolutional neural networks to predict labels for nodes and entire graph structures.

The library provides specialized tools for spectral graph convolutions, utilizing Chebyshev polynomial approximations to perform feature aggregation. It includes a multi-graph processing framework that manages batches of different graph instances through block-diagonal adjacency matrices and pooling
- [shenweichen/graphembedding](https://awesome-repositories.com/repository/shenweichen-graphembedding.md) (3,844 ⭐) — GraphEmbedding is a graph network representation library and node embedding framework. It provides a toolkit for transforming complex network nodes into low-dimensional vector spaces, enabling the integration of relational graph data into machine learning workflows.

The library functions as a dimensionality reduction toolkit and network topology analysis tool. It uses matrix-factorization techniques to preserve global connectivity and employs random-walk sampling with skip-gram based vector optimization to learn numerical representations of nodes.

The framework covers several domain-specific
- [seongjunyun/graph_transformer_networks](https://awesome-repositories.com/repository/seongjunyun-graph-transformer-networks.md) (1,082 ⭐) — Graph_Transformer_Networks is a graph neural network framework implemented in PyTorch for learning structural representations and performing classification tasks on complex heterogeneous graphs and relational networks. The project provides automated preprocessing pipelines to transform raw graph datasets into standardized formats, alongside model training, forward passes, and gradient backpropagation executed through dynamic tensor operations. 

The architecture incorporates self-attention mechanisms applied directly to graph structures to learn contextual representations of nodes and edges ac
- [alibaba/graph-learn](https://awesome-repositories.com/repository/alibaba-graph-learn.md) (1,341 ⭐) — Graph-learn is a distributed graph processing engine and graph neural network framework designed for large-scale graph data. It provides specialized query interfaces to extract training subgraphs and node neighborhoods, enabling the construction and training of complex graph neural network models on massive datasets. The system integrates a real-time inference server to serve live predictions by sampling dynamic graphs with low latency while processing streaming graph updates.

The project features a C++ core engine integration that executes graph sampling and tensor operations natively, coupl
- [dsgiitr/graph_nets](https://awesome-repositories.com/repository/dsgiitr-graph-nets.md) (1,237 ⭐) — Graph Nets is a graph neural network library and educational toolkit implemented in PyTorch, providing implementations of popular graph representation learning algorithms and research papers. The project covers core graph machine learning tasks including semi-supervised node classification, inductive and unsupervised node embedding generation, and neighborhood feature aggregation.

The library supports diverse algorithmic approaches for processing network structures, ranging from shared-parameter graph convolutions and attention-weighted neighborhood aggregation to spectral Chebyshev filtering
- [mdeff/cnn_graph](https://awesome-repositories.com/repository/mdeff-cnn-graph.md) (1,369 ⭐) — Cnn_graph is a graph convolutional network framework and graph signal processing library designed for machine learning research. It provides computational notebooks and code to process and classify graph-structured data by combining node features with an underlying adjacency matrix representation.

The framework performs spectral graph convolutions through localized filters and accelerates filtering operations using truncated Chebyshev polynomials to avoid explicit graph Laplacian diagonalization. It includes a graph-structured data pipeline and sparse adjacency representations to handle irreg